Image post-processes, such as reconstruction, denoising, inpainting, image supperresolution, and low light image enhancement require image quality assessment in order to render a post-processed image with the highest perceptual quality. However, image quality assessment is often time-consuming and expensive when performed by hand. One way to reduce the time and cost required for image quality assessment is to use an image quality assessment algorithm. The strength of an image quality assessment algorithm is measured by its agreement with the human visual system.
The fastest and most accurate image quality assessment algorithms generally use a full-reference or reduced-reference image quality index to select one or more parameters required by the restoration process. Full-reference indices utilize methods such as a mean squared error method, a signal-to-noise ratio method, or a structural similarity index method. However, these algorithms require a reference image, which is not always available.
A reduced-reference image quality index does not require a full reference image but utilizes statistical features from a reference image, such as the power spectrum. Reduced-reference assessment is generally slower and less accurate than full-reference assessment. Moreover, reduced-reference assessment is not possible when statistical features of the reference image are unknown or unavailable.
When no reference image is available, a no-reference image quality index may be used to select one or more parameters required by the restoration process.
The benefit of such an index is that image quality assessment can be performed based on a single distorted image. However, no-reference assessment is generally slower than full-reference or reduced-reference assessment and is generally less accurate than full-reference or reduced-reference assessment. Therefore, there is a need for an assessment algorithm that is fast and accurate but, like traditional no-reference assessment, requires no reference image.